Abstract
The generative approach to social science, in which agent-based simulations (or other complex systems models) are executed to reproduce a known social phenomenon, is an important tool for realist explanation. However, a generative model, when suitably calibrated and validated using empirical data, represents just one viable candidate set of entities and mechanisms. The model only partially addresses the needs of an abductive reasoning process - specifically it does not provide insight into other viable sets of entities or mechanisms, nor suggest which of these are fundamentally constitutive for the phenomenon to exist. In this paper, we propose a new model discovery framework that more fully captures the needs of realist explanation. The framework exploits the implicit ontology of an existing human-built generative model to propose and test a plurality of new candidate model structures. Genetic programming is used to automate this search process. A multi-objective approach is used, which enables multiple perspectives on the value of any particular generative model - such as goodness-of-fit, parsimony, and interpretability - to be represented simultaneously. We demonstrate this new framework using a complex systems modeling case study of change and stasis in societal alcohol use patterns in the US over the period 1980-2010. The framework is successful in identifying three competing explanations of these alcohol use patterns, using novel integrations of social role theory not previously considered by the human modeler. Practitioners in complex systems modeling should use model discovery to improve the explanatory utility of the generative approach to realist social science.
Highlights
Agent-based simulation (ABS) is a well-established tool for understanding complex systems using the generative social science approach. e goal of generative social science is to explain and understand a social phenomenon as the result of actions of autonomous entities acting according to causal mechanisms or rules as encoded in an agent-based model [1]
Many social phenomena may be explained by a multiplicity of theories, each of which could pass the generative test when encoded as an ABS, leaving us to wonder which theory is correct; how can theories be combined; and what is missing from our theories? Here, we propose a novel method of discovering new models and extending the explanatory capabilities of theory-driven generative models using multiobjective genetic programming—a process of knowledge discovery
Modifications were made to the grammar between each iteration in an attempt to improve the effectiveness of the discovery process. is was an openended trial and error iterative process involving the modeler and the analyst
Summary
Agent-based simulation (ABS) is a well-established tool for understanding complex systems using the generative social science approach. e goal of generative social science is to explain and understand a social phenomenon as the result of actions of autonomous entities acting according to causal mechanisms or rules as encoded in an agent-based model [1]. Agent-based simulation (ABS) is a well-established tool for understanding complex systems using the generative social science approach. E goal of generative social science is to explain and understand a social phenomenon as the result of actions of autonomous entities acting according to causal mechanisms or rules as encoded in an agent-based model [1]. We set out the following aims: (1) to explain the role of complex systems models for realist explanation; (2) to define the structural calibration method: a retroductive model discovery framework; (3) to demonstrate the application of the model discovery framework to a specific mechanism-based social systems model; and (4) to discuss the implications of computer aided model discovery in light of the case study results
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